Algorithm 1. Noise removal of the local speckle noise.
1:	Begin
2:	Logarithmic and computational transforms are used to improve the differentiation of the input ultrasound breast images; the algorithm (guided filter) is used to improve the details of the glandular ultrasound images; and the spatial high-pass filtering algorithm is used to denoise the over-sharpening of the ultrasound breast images, all based on their grayscale values
3:	The pre-processed ultrasound breast images are fed into a local-speckle-noise destruction model of a logical-pool recurrent neural network
4:	Ultrasound breast images are susceptible to losing image edge information during the local speckle noise reduction procedure. If we want to preserve the edge information after local speckle noise removal is applied, we will need to understand how that information is lost during processing. The meaning of “edge information loss”. $${\mathrm{Loss}}_{\mathrm{Edge}}\left(\mathrm{P}\right)=\log \frac{{\mathrm{I}}_{\mathrm{m}}^{\text{″}}\left(\mathrm{a},\mathrm{b}\right)}{{{\displaystyle \sum }}_{\mathrm{i},\mathrm{j}}\left|{\mathrm{b}}_{\mathrm{i}+1,\mathrm{j}}-{\mathrm{b}}_{\mathrm{i},\mathrm{j}}\right|}$$ (12)
5:	In order to construct ultrasound image gradients, we first analyze the aforementioned stages and then use edge loss pairs to compare the edges of canonical clear images of ultrasound breast images. The unique anatomy of the breast emphasizes the significance of the gradients in the vertical plane. That is why we first use contrast in the vertical direction to depict breast ultrasound images. Integrating edge loss ${\mathrm{Loss}}_{\mathrm{Edge}}\left(\mathrm{P}\right)$ and L1 distance with a recurrent neural network yields the following objective function: $${\mathrm{P}}^{\ast },{ \mathrm{C}}^{\ast }=\arg \underset{\mathrm{P}}{\min }\underset{\mathrm{C}}{\max }{ \mathrm{Loss}}_{\mathrm{HRNN}}\left(\mathrm{P},\mathrm{C}\right)+{\mathrm{b} \mathrm{Loss}}_{{\mathrm{Loss}}_1}\left(\mathrm{P}\right)+{\mathsf{\beta }\mathrm{Loss}}_{\mathrm{Edge}}\left(\mathrm{P}\right)$$ (13)
6:	Enhance the loss function to optimize the edge-specific improvement feature of the ultrasound images during training with the logical-pool recurrent neural network. The resulting model will be more responsive in edge local speckle noise destruction in ultrasound images, enhancing its effect on ultrasound breast images
7:	While noise removal reduces the local speckle noise of ultrasound breast images, the edge information is preserved by the action of the advantage term in the logical-pool recurrent neural network as described above
8:	End